"""

>>> from blaze import *

>>> accounts = symbol('accounts', 'var * {name: string, amount: int}')

>>> deadbeats = accounts[accounts['amount'] < 0]['name']

>>> from sqlalchemy import Table, Column, MetaData, Integer, String

>>> t = Table('accounts', MetaData(),

... Column('name', String, primary_key = True),

... Column('amount', Integer))

>>> print(compute(deadbeats, t)) # doctest: +SKIP

SELECT accounts.name

FROM accounts

WHERE accounts.amount < :amount_1

"""

from __future__ import absolute_import, division, print_function

from copy import copy

import datetime

import itertools

from itertools import chain

from operator import and_, eq, attrgetter

import warnings

from datashape import TimeDelta, Option, int32

from datashape.predicates import iscollection, isscalar, isrecord

import numpy as np

import numbers

from odo.backends.sql import metadata_of_engine, dshape_to_alchemy

from multipledispatch import MDNotImplementedError

import sqlalchemy as sa

from sqlalchemy import sql, Table, MetaData

from sqlalchemy.engine import Engine

from sqlalchemy.sql import Selectable, Select, functions as safuncs

from sqlalchemy.ext.compiler import compiles

from sqlalchemy.sql.elements import ClauseElement, ColumnElement, ColumnClause

from sqlalchemy.sql.selectable import FromClause, ScalarSelect

import toolz

from toolz import unique, concat, pipe, first

from toolz.compatibility import zip

from toolz.curried import map

from .core import compute_up, compute, base

from ..compatibility import reduce, basestring, _inttypes

from ..dispatch import dispatch

from ..expr import (

BinOp,

BinaryMath,

Broadcast,

By,

Coalesce,

Coerce,

Concat,

DateTime,

DateTimeTruncate,

Distinct,

Expr,

Field,

FloorDiv,

Head,

IsIn,

Join,

Label,

Like,

Merge,

Pow,

Projection,

ReLabel,

Reduction,

Sample,

Selection,

Shift,

Slice,

Sort,

Sub,

Summary,

Tail,

UnaryOp,

UnaryStringFunction,

common_subexpression,

count,

greatest,

least,

mean,

nelements,

notnull,

nunique,

reductions,

std,

StrCat,

StrFind,

StrSlice,

var,

)

from ..expr.strings import len as str_len

from ..expr.broadcast import broadcast_collect

from ..expr.math import isnan

from ..utils import listpack

__all__ = ['sa', 'select']

def inner_columns(s):

try:

return s.inner_columns

except AttributeError:

return s.columns

@dispatch(Projection, Select)

def compute_up(expr, data, **kwargs):

d = dict((c.name, c) for c in getattr(data, 'inner_columns', data.c))

return data.with_only_columns([d[field] for field in expr.fields])

@dispatch(Projection, Selectable)

def compute_up(expr, data, **kwargs):

return compute(

expr,

sa.select([data]),

post_compute=False,

return_type='native',

)

@dispatch(Projection, sa.Column)

def compute_up(expr, data, **kwargs):

selectables = [

compute(

expr._child[field],

data,

post_compute=False,

return_type='native',

)

for field in expr.fields

]

froms = set(concat(s.froms for s in selectables))

assert 1 <= len(froms) <= 2

result = unify_froms(sa.select(first(sel.inner_columns)

for sel in selectables),

froms)

return result.where(unify_wheres(selectables))

@dispatch(Projection, Select)

def compute_up(expr, data, **kwargs):

return data.with_only_columns(

first(compute(

expr._child[field],

data,

post_compute=False,

return_type='native',

).inner_columns)

for field in expr.fields

)

@dispatch(Field, FromClause)

def compute_up(t, s, **kwargs):

return s.c[t._name]

def unify_froms(select, selectables):

return reduce(lambda x, y: x.select_from(y), selectables, select)

def unify_wheres(selectables):

clauses = list(unique((s._whereclause for s in selectables

if hasattr(s, '_whereclause')), key=str))

return reduce(and_, clauses) if clauses else None

@dispatch(Field, Select)

def compute_up(expr, data, **kwargs):

name = expr._name

try:

inner_columns = list(data.inner_columns)

names = list(c.name for c in data.inner_columns)

column = inner_columns[names.index(name)]

except (KeyError, ValueError):

single_column_select = compute(

expr,

first(data.inner_columns),

post_compute=False,

return_type='native',

)

column = first(single_column_select.inner_columns)

result = unify_froms(sa.select([column]),

data.froms + single_column_select.froms)

return result.where(unify_wheres([data, single_column_select]))

else:

return data.with_only_columns([column])

@dispatch(Field, sa.Column)

def compute_up(t, s, **kwargs):

assert len(s.foreign_keys) == 1, 'exactly one foreign key allowed'

key_col = first(s.foreign_keys).column

return sa.select([key_col.table.c[t._name]]).where(s == key_col)

@dispatch(Broadcast, Select)

def compute_up(t, s, **kwargs):

cols = list(inner_columns(s))

d = dict((t._scalars[0][c], cols[i])

for i, c in enumerate(t._scalars[0].fields))

result = compute(

t._scalar_expr,

d,

post_compute=False,

return_type='native',

).label(t._name)

s = copy(s)

s.append_column(result)

return s.with_only_columns([result])

@dispatch(Broadcast, Selectable)

def compute_up(t, s, **kwargs):

cols = list(inner_columns(s))

d = dict((t._scalars[0][c], cols[i])

for i, c in enumerate(t._scalars[0].fields))

return compute(

t._scalar_expr,

d,

post_compute=False,

return_type='native',

).label(t._name)

@dispatch(Concat, (Select, Selectable), (Select, Selectable))

def compute_up(t, lhs, rhs, **kwargs):

if t.axis != 0:

raise ValueError(

'Cannot concat along a non-zero axis in sql; perhaps you want'

" 'merge'?",

)

return select(lhs).union_all(select(rhs)).alias()

@dispatch(Broadcast, ColumnElement)

def compute_up(t, s, **kwargs):

expr = t._scalar_expr

return compute(

expr,

s,

post_compute=False,

return_type='native',

).label(expr._name)

def _binop(type_, f):

@dispatch(type_, ColumnElement)

def compute_up(t, data, **kwargs):

if isinstance(t.lhs, Expr):

return t.op(data, t.rhs)

else:

return f(t, t.lhs, data)

@dispatch(type_, Select)

def compute_up(t, data, **kwargs):

assert len(data.c) == 1, (

'Select cannot have more than a single column when doing'

' arithmetic'

)

column = first(data.inner_columns)

if isinstance(t.lhs, Expr):

return f(t, column, t.rhs)

else:

return f(t, t.lhs, column)

@compute_up.register(type_,

(Select, ColumnElement, base),

(Select, ColumnElement))

@compute_up.register(type_,

(Select, ColumnElement),

base)

def binop_sql(t, lhs, rhs, **kwargs):

if isinstance(lhs, Select):

assert len(lhs.c) == 1, (

'Select cannot have more than a single column when doing'

' arithmetic, got %r' % lhs

)

lhs = first(lhs.inner_columns)

if isinstance(rhs, Select):

assert len(rhs.c) == 1, (

'Select cannot have more than a single column when doing'

' arithmetic, got %r' % rhs

)

rhs = first(rhs.inner_columns)

return f(t, lhs, rhs)

_binop(BinOp, lambda expr, lhs, rhs: expr.op(lhs, rhs))

_binop(

(greatest, least),

lambda expr, lhs, rhs: getattr(sa.func, type(expr).__name__)(lhs, rhs),

)

@dispatch(Pow, ColumnElement)

def compute_up(t, data, **kwargs):

if isinstance(t.lhs, Expr):

return sa.func.pow(data, t.rhs)

else:

return sa.func.pow(t.lhs, data)

@dispatch(Pow, Select)

def compute_up(t, data, **kwargs):

assert len(data.c) == 1, (

'Select cannot have more than a single column when doing'

' arithmetic, got %r' % data

)

column = first(data.inner_columns)

if isinstance(t.lhs, Expr):

return sa.func.pow(column, t.rhs)

else:

return sa.func.pow(t.lhs, column)

@compute_up.register(Pow, (ColumnElement, base), ColumnElement)

@compute_up.register(Pow, ColumnElement, base)

def binop_sql_pow(t, lhs, rhs, **kwargs):

return sa.func.pow(lhs, rhs)

@dispatch(BinaryMath, ColumnElement)

def compute_up(t, data, **kwargs):

op = getattr(sa.func, type(t).__name__)

if isinstance(t.lhs, Expr):

return op(data, t.rhs)

else:

return op(t.lhs, data)

@dispatch(BinaryMath, Select)

def compute_up(t, data, **kwargs):

assert len(data.c) == 1, (

'Select cannot have more than a single column when doing'

' arithmetic, got %r' % data

)

column = first(data.inner_columns)

op = getattr(sa.func, type(t).__name__)

if isinstance(t.lhs, Expr):

return op(column, t.rhs)

else:

return op(t.lhs, column)

@compute_up.register(BinaryMath, (ColumnElement, base), ColumnElement)

@compute_up.register(BinaryMath, ColumnElement, base)

def binary_math_sql(t, lhs, rhs, **kwargs):

return getattr(sa.func, type(t).__name__)(lhs, rhs)

@compute_up.register(BinaryMath, Select, base)

def binary_math_sql_select(t, lhs, rhs, **kwargs):

left, right = first(lhs.inner_columns), rhs

result = getattr(sa.func, type(t).__name__)(left, right)

return reconstruct_select([result], lhs)

@compute_up.register(BinaryMath, base, Select)

def binary_math_sql_select(t, lhs, rhs, **kwargs):

left, right = lhs, first(rhs.inner_columns)

result = getattr(sa.func, type(t).__name__)(left, right)

return reconstruct_select([result], rhs)

@compute_up.register(BinaryMath, Select, Select)

def binary_math_sql_select(t, lhs, rhs, **kwargs):

left, right = first(lhs.inner_columns), first(rhs.inner_columns)

result = getattr(sa.func, type(t).__name__)(left, right)

assert lhs.table == rhs.table

return reconstruct_select([result], lhs.table)

@dispatch(FloorDiv, ColumnElement)

def compute_up(t, data, **kwargs):

if isinstance(t.lhs, Expr):

return sa.func.floor(data / t.rhs)

else:

return sa.func.floor(t.rhs / data)

@compute_up.register(FloorDiv, (ColumnElement, base), ColumnElement)

@compute_up.register(FloorDiv, ColumnElement, base)

def binop_sql(t, lhs, rhs, **kwargs):

return sa.func.floor(lhs / rhs)

@dispatch(isnan, ColumnElement)

def compute_up(t, s, **kwargs):

return s == float('nan')

@dispatch(UnaryOp, ColumnElement)

def compute_up(t, s, **kwargs):

sym = t.symbol

return getattr(t, 'op', getattr(safuncs, sym, getattr(sa.func, sym)))(s)

@dispatch(Selection, sa.sql.ColumnElement)

def compute_up(expr, data, scope=None, **kwargs):

predicate = compute(

expr.predicate,

data,

post_compute=False,

return_type='native',

)

return compute(

expr,

{expr._child: data, expr.predicate: predicate},

return_type='native',

**kwargs

)

@dispatch(Selection, sa.sql.ColumnElement, ColumnElement)

def compute_up(expr, col, predicate, **kwargs):

return sa.select([col]).where(predicate)

@dispatch(Selection, Selectable)

def compute_up(expr, sel, scope=None, **kwargs):

return compute(

expr,

{

expr._child: sel,

expr.predicate: compute(

expr.predicate,

toolz.merge(

{

expr._child[col.name]: col

for col in getattr(sel, 'inner_columns', sel.columns)

},

scope,

),

optimize=False,

post_compute=False,

),

},

return_type='native',

**kwargs

)

@dispatch(Selection, Selectable, ColumnElement)

def compute_up(expr, tbl, predicate, scope=None, **kwargs):

try:

return tbl.where(predicate)

except AttributeError:

return select([tbl]).where(predicate)

@dispatch(Selection, Selectable, Selectable)

def compute_up(expr, tbl, predicate, **kwargs):

col, = inner_columns(predicate)

return reconstruct_select(

inner_columns(tbl),

tbl,

whereclause=unify_wheres((tbl, predicate)),

).where(col)

def select(s):

""" Permissive SQL select

Idempotent sa.select

Wraps input in list if neccessary

"""

if not isinstance(s, sa.sql.Select):

if not isinstance(s, (tuple, list)):

s = [s]

s = sa.select(s)

return s

table_names = ('table_%d' % i for i in itertools.count(1))

def name(sel):

""" Name of a selectable """

if hasattr(sel, 'name'):

return sel.name

if hasattr(sel, 'froms'):

if len(sel.froms) == 1:

return name(sel.froms[0])

return next(table_names)

@dispatch(Select, Select)

def _join_selectables(a, b, condition=None, **kwargs):

return a.join(b, condition, **kwargs)

@dispatch(Select, ClauseElement)

def _join_selectables(a, b, condition=None, **kwargs):

if len(a.froms) > 1:

raise MDNotImplementedError()

return a.replace_selectable(a.froms[0],

a.froms[0].join(b, condition, **kwargs))

@dispatch(ClauseElement, Select)

def _join_selectables(a, b, condition=None, **kwargs):

if len(b.froms) > 1:

raise MDNotImplementedError()

return b.replace_selectable(b.froms[0],

a.join(b.froms[0], condition, **kwargs))

@dispatch(ClauseElement, ClauseElement)

def _join_selectables(a, b, condition=None, **kwargs):

return a.join(b, condition, **kwargs)

_getname = attrgetter('name')

def _clean_join_name(opposite_side_colnames, suffix, c):

if c.name not in opposite_side_colnames:

return c

else:

return c.label(c.name + suffix)

@dispatch(Join, ClauseElement, ClauseElement)

def compute_up(t, lhs, rhs, **kwargs):

if isinstance(lhs, ColumnElement):

lhs = select(lhs)

if isinstance(rhs, ColumnElement):

rhs = select(rhs)

if name(lhs) == name(rhs):

left_suffix, right_suffix = t.suffixes

lhs = lhs.alias('%s%s' % (name(lhs), left_suffix))

rhs = rhs.alias('%s%s' % (name(rhs), right_suffix))

lhs = alias_it(lhs)

rhs = alias_it(rhs)

if isinstance(lhs, Select):

lhs = lhs.alias(next(aliases))

left_conds = [lhs.c.get(c) for c in listpack(t.on_left)]

else:

ldict = dict((c.name, c) for c in inner_columns(lhs))

left_conds = [ldict.get(c) for c in listpack(t.on_left)]

if isinstance(rhs, Select):

rhs = rhs.alias(next(aliases))

right_conds = [rhs.c.get(c) for c in listpack(t.on_right)]

else:

rdict = dict((c.name, c) for c in inner_columns(rhs))

right_conds = [rdict.get(c) for c in listpack(t.on_right)]

condition = reduce(and_, map(eq, left_conds, right_conds))

# Perform join

if t.how == 'inner':

join = _join_selectables(lhs, rhs, condition=condition)

main = lhs

elif t.how == 'left':

main, other = lhs, rhs

join = _join_selectables(lhs, rhs, condition=condition, isouter=True)

elif t.how == 'right':

join = _join_selectables(rhs, lhs, condition=condition, isouter=True)

main = rhs

else:

# http://stackoverflow.com/questions/20361017/sqlalchemy-full-outer-join

raise ValueError("SQLAlchemy doesn't support full outer Join")

"""

We now need to arrange the columns in the join to match the columns in

the expression. We care about order and don't want repeats

"""

if isinstance(join, Select):

def cols(x):

if isinstance(x, Select):

return list(x.inner_columns)

else:

return list(x.columns)

else:

cols = lambda x: list(x.columns)

main_cols = cols(main)

left_cols = cols(lhs)

left_names = set(map(_getname, left_cols))

right_cols = cols(rhs)

right_names = set(map(_getname, right_cols))

left_suffix, right_suffix = t.suffixes

fields = [

f.replace(left_suffix, '').replace(right_suffix, '') for f in t.fields

]

columns = [c for c in main_cols if c.name in t._on_left]

columns += [_clean_join_name(right_names, left_suffix, c)

for c in left_cols

if c.name in fields and c.name not in t._on_left]

columns += [_clean_join_name(left_names, right_suffix, c)

for c in right_cols

if c.name in fields and c.name not in t._on_right]

if isinstance(join, Select):

return join.with_only_columns(columns)

else:

return sa.select(columns, from_obj=join)

names = {

mean: 'avg'

}

def reconstruct_select(columns, original, **kwargs):

return sa.select(columns,

from_obj=kwargs.pop('from_obj', None),

whereclause=kwargs.pop('whereclause',

getattr(original,

'_whereclause', None)),

bind=kwargs.pop('bind', original.bind),

distinct=kwargs.pop('distinct',

getattr(original,

'_distinct', False)),

group_by=kwargs.pop('group_by',

getattr(original,

'_group_by_clause', None)),

having=kwargs.pop('having',

getattr(original, '_having', None)),

limit=kwargs.pop('limit',

getattr(original, '_limit', None)),

offset=kwargs.pop('offset',

getattr(original, '_offset', None)),

order_by=kwargs.pop('order_by',

getattr(original,

'_order_by_clause', None)),

**kwargs)

@dispatch((nunique, Reduction), Select)

def compute_up(expr, data, **kwargs):

if expr.axis != (0,):

raise ValueError('axis not equal to 0 not defined for SQL reductions')

data = data.alias(name=next(aliases))

cols = list(inner_columns(data))

d = dict((expr._child[c], cols[i])

for i, c in enumerate(expr._child.fields))

return select([

compute(expr, d, post_compute=False, return_type='native')

])

@dispatch(Distinct, ColumnElement)

def compute_up(t, s, **kwargs):

return s.distinct(*t.on).label(t._name)

@dispatch(Distinct, Select)

def compute_up(t, s, **kwargs):

return s.distinct(*t.on)

@dispatch(Distinct, Selectable)

def compute_up(t, s, **kwargs):

return select(s).distinct(*t.on)

@dispatch(Reduction, ClauseElement)

def compute_up(t, s, **kwargs):

if t.axis != (0,):

raise ValueError('axis not equal to 0 not defined for SQL reductions')

try:

op = getattr(sa.sql.functions, t.symbol)

except AttributeError:

op = getattr(sa.sql.func, names.get(type(t), t.symbol))

return op(s).label(t._name)

prefixes = {

std: 'stddev',

var: 'var'

}

@dispatch((std, var), sql.elements.ColumnElement)

def compute_up(t, s, **kwargs):

measure = t.schema.measure

is_timedelta = isinstance(getattr(measure, 'ty', measure), TimeDelta)

if is_timedelta:

# part 1 of 2 to work around the fact that postgres does not have

# timedelta var or std: cast to a double which is seconds

s = sa.extract('epoch', s)

if t.axis != (0,):

raise ValueError('axis not equal to 0 not defined for SQL reductions')

funcname = 'samp' if t.unbiased else 'pop'

full_funcname = '%s_%s' % (prefixes[type(t)], funcname)

ret = getattr(sa.func, full_funcname)(s)

if is_timedelta:

# part 2 of 2 to work around the fact that postgres does not have

# timedelta var or std: cast back from seconds by

# multiplying by a 1 second timedelta

ret = ret * datetime.timedelta(seconds=1)

return ret.label(t._name)

@dispatch(count, Selectable)

def compute_up(t, s, **kwargs):

return s.count()

@dispatch(count, sa.Table)

def compute_up(t, s, **kwargs):

if t.axis != (0,):

raise ValueError('axis not equal to 0 not defined for SQL reductions')

try:

c = list(s.primary_key)[0]

except IndexError:

c = list(s.columns)[0]

return sa.func.count(c)

@dispatch(nelements, (Select, ClauseElement))

def compute_up(t, s, **kwargs):

return compute_up(t._child.count(), s)

@dispatch(count, Select)

def compute_up(t, s, **kwargs):

if t.axis != (0,):

raise ValueError('axis not equal to 0 not defined for SQL reductions')

al = next(aliases)

try:

s2 = s.alias(al)

col = list(s2.primary_key)[0]

except (KeyError, IndexError):

s2 = s.alias(al)

col = list(s2.columns)[0]

result = sa.func.count(col)

return select([list(inner_columns(result))[0].label(t._name)])

@dispatch(nunique, (sa.sql.elements.Label, sa.Column))

def compute_up(t, s, **kwargs):

if t.axis != (0,):

raise ValueError('axis not equal to 0 not defined for SQL reductions')

return sa.func.count(s.distinct())

@dispatch(nunique, Selectable)

def compute_up(expr, data, **kwargs):

return select(data).distinct().alias(next(aliases)).count()

@dispatch(By, sa.Column)

def compute_up(expr, data, scope=None, **kwargs):

data = lower_column(data)

grouper = compute(

expr.grouper,

scope,

post_compute=False,

return_type='native',

**kwargs

)

app = expr.apply

reductions = [

compute(

val,

data,

post_compute=None,

return_type='native',

).label(name)

for val, name in zip(app.values, app.fields)

]

froms = list(unique(chain(get_all_froms(grouper),

concat(map(get_all_froms, reductions)))))

inner_cols = list(getattr(grouper, 'inner_columns', [grouper]))

grouper_cols = inner_cols[:]

inner_cols.extend(concat(

getattr(getattr(r, 'element', None), 'inner_columns', [r])

for r in reductions

))

wheres = unify_wheres([grouper] + reductions)

sel = unify_froms(sa.select(inner_cols, whereclause=wheres), froms)

return sel.group_by(*grouper_cols)

@dispatch(By, ClauseElement)

def compute_up(expr, data, **kwargs):

if not valid_grouper(expr.grouper):

raise TypeError("Grouper must have a non-nested record or one "

"dimensional collection datashape, "

"got %s of type %r with dshape %s" %

(expr.grouper, type(expr.grouper).__name__,

expr.grouper.dshape))

grouper = get_inner_columns(

compute(

expr.grouper,

data,

post_compute=False,

return_type='native',

),

)

app = expr.apply

reductions = [

compute(

val,

data,

post_compute=False,

return_type='native',

).label(name)

for val, name in zip(app.values, app.fields)

]

return sa.select(grouper + reductions).group_by(*grouper)

def lower_column(col):

""" Return column from lower level tables if possible

>>> metadata = sa.MetaData()

>>> s = sa.Table('accounts', metadata,

... sa.Column('name', sa.String),

... sa.Column('amount', sa.Integer),

... sa.Column('id', sa.Integer, primary_key=True),

... )

>>> s2 = select([s])

>>> s2.c.amount is s.c.amount

False

>>> lower_column(s2.c.amount) is s.c.amount

True

>>> lower_column(s2.c.amount)

Column('amount', Integer(), table=<accounts>)

"""

old = None

while col is not None and col is not old:

old = col

if not hasattr(col, 'table') or not hasattr(col.table, 'froms'):

return col

for f in col.table.froms:

if f.corresponding_column(col) is not None:

col = f.corresponding_column(col)

return old

aliases = ('alias_%d' % i for i in itertools.count(1))

@toolz.memoize

def alias_it(s):

""" Alias a Selectable if it has a group by clause """

if (hasattr(s, '_group_by_clause') and

s._group_by_clause is not None and

len(s._group_by_clause)):

return s.alias(next(aliases))

else:

return s

def is_nested_record(measure):

"""Predicate for checking whether `measure` is a nested ``Record`` dshape

Examples

--------

>>> from datashape import dshape

>>> is_nested_record(dshape('{a: int32, b: int32}').measure)

False

>>> is_nested_record(dshape('{a: var * ?float64, b: ?string}').measure)

True

"""

if not isrecord(measure):

raise TypeError('Input must be a Record type got %s of type %r' %

(measure, type(measure).__name__))

return not all(isscalar(getattr(t, 'key', t)) for t in measure.types)

def valid_grouper(expr):

ds = expr.dshape

measure = ds.measure

return (iscollection(ds) and

(isscalar(getattr(measure, 'key', measure)) or

(isrecord(measure) and not is_nested_record(measure))))

def valid_reducer(expr):

ds = expr.dshape

measure = ds.measure

return (not iscollection(ds) and

(isscalar(measure) or

(isrecord(measure) and not is_nested_record(measure))))

@dispatch(By, Select)

def compute_up(expr, data, **kwargs):

if not valid_grouper(expr.grouper):

raise TypeError("Grouper must have a non-nested record or one "

"dimensional collection datashape, "

"got %s of type %r with dshape %s" %

(expr.grouper, type(expr.grouper).__name__,

expr.grouper.dshape))

s = alias_it(data)

if valid_reducer(expr.apply):

reduction = compute(

expr.apply,

s,

post_compute=False, return_type='native',

)

else:

raise TypeError('apply must be a Summary expression')

grouper = get_inner_columns(compute(

expr.grouper,

s,

post_compute=False,

return_type='native',

))

reduction_columns = pipe(reduction.inner_columns,

map(get_inner_columns),

concat)

columns = list(unique(chain(grouper, reduction_columns)))

if (not isinstance(s, sa.sql.selectable.Alias) or

(hasattr(s, 'froms') and isinstance(s.froms[0],

sa.sql.selectable.Join))):

assert len(s.froms) == 1, 'only a single FROM clause supported for now'

from_obj, = s.froms

else:

from_obj = None

return reconstruct_select(columns,

getattr(s, 'element', s),

from_obj=from_obj,

group_by=grouper)

@dispatch(Sort, (Selectable, Select))

def compute_up(t, s, **kwargs):

s = select(s.alias())

direction = sa.asc if t.ascending else sa.desc

cols = [direction(lower_column(s.c[c])) for c in listpack(t.key)]

return s.order_by(*cols)

@dispatch(Sort, (sa.Table, ColumnElement))

def compute_up(t, s, **kwargs):

s = select(s)

direction = sa.asc if t.ascending else sa.desc

cols = [direction(lower_column(s.c[c])) for c in listpack(t.key)]

return s.order_by(*cols)

def _samp_compute_up(t, s, **kwargs):

if t.n is not None:

limit = t.n

else:

limit = sa.select([safuncs.count() * t.frac],

from_obj=s.alias()).as_scalar()

return s.order_by(safuncs.random()).limit(limit)

@dispatch(Sample, sa.Table)

def compute_up(t, s, **kwargs):

return _samp_compute_up(t, select(s), **kwargs)

@dispatch(Sample, ColumnElement)

def compute_up(t, s, **kwargs):

return _samp_compute_up(t, sa.select([s]), **kwargs)

@dispatch(Sample, FromClause)

def compute_up(t, s, **kwargs):

return _samp_compute_up(t, s, **kwargs)

@dispatch(Head, FromClause)

def compute_up(t, s, **kwargs):

if s._limit is not None and s._limit <= t.n:

return s

return s.limit(t.n)